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In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial "PCA and K-meansВ decipher genome". The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics.
Principal manifolds for data visualization and dimension reduction Автор: Gorban A., Kegl B., Wunsch D., et al. (eds.) Год: 2007 |
Encyclopedia of Life Sciences Автор: John Wiley&Sons Ltd Год: 2010 |
Chemistry: An Introduction for Medical and Health Sciences Автор: Alan Jones Год: 2005 |
Advanced Tutorials for the Biomedical Sciences Автор: Charles Pidgeon Год: 1996 |
Fluorescence Applications in Biotechnology and Life Sciences Автор: Ewa M. Goldys Год: 2009 |
Physical Processes in Earth and Environmental Sciences Автор: Professor Mike R Leeder Год: 2005 |